Fault diagnosis model of rolling bearing based on parameter adaptive AVMD algorithm

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 53; no. 3; pp. 3150 - 3165
• Main Authors: Li, Meixuan, Yan, Chun, Liu, Wei, Liu, Xinhong, Zhang, Mengchao, Xue, Jiankai
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Bearing strength; Computer Science; Decomposition; Fault diagnosis; Feature extraction; Kurtosis; Machines; Manufacturing; Mathematical models; Mechanical Engineering; Parameters; Processes; Roller bearings; Search algorithms; Search process; Support vector machines; Fault diagnosis; Sample entropy; Variational modal decomposition; Intelligent optimization; Sparrow search algorithm; Rolling bearing
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-022-03562-9

Aiming at the intrinsic aspect that the weak features of the early fault information of rolling bearings are not easy to extract, a parameter Adaptive Variational Modal Decomposition (AVMD) based algorithm is proposed for bearing fault signal feature extraction. Since the number of Variational Modal Decomposition (VMD) decomposition and penalty factor play an important role in VMD decomposition effect, the irregularities in the selection of these two influencing parameters are analyzed. We exploit the stronger global search capability of the improved sparrow search algorithm (LSSA) for adaptive parameter selection of the VMD algorithm. In this paper, the Levy flight algorithm is introduced and chaos is added to initialize sparrow population position to prevent sparrow from falling into the disadvantage of local optimum in the search process. In addition, this paper also combines the maximum kurtosis index, the minimum envelope entropy index and the number of iterations of VMD to form the objective function of the LSSA. The VMD algorithm with optimized parameters decomposes the signal to be measured, the decomposed IMFs was reconstructed, finally the validity of the model was verified by calculating 20 features (time domain and frequency domain) of the reconstructed signal as the input vector of the SVM classifier. Finally, the feasibility of this model in fault diagnosis of rolling bearing is verified using simulation and example experiments.